期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 72, 期 -, 页码 306-316出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2016.10.058
关键词
Machine learning; Expected values; Moving averages; Cell properties; Perturbation theory; Time series analysis
类别
资金
- National Natural Science Foundation of China [31172234, 31260556]
- Planned Science and Technology Project of Hunan Province [2015NK3041]
- Strategic Priority Research Program - Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Science [XDA05020700]
- Hunan Provincial Creation Development Project [2013TF3006]
- Key Laboratory of Subtropical Agro-ecological Engineering, Institute of Subtropical Agriculture
- General Directorate of Culture, Education and University Management of Xunta de Galicia, Spain [GRC2014/049]
- Spanish Ministry of Economy and Competitiveness
- BIOCAI [UNLC08-1E-002, UNLC13-13-3503]
- European Regional Development Funds (FEDER) by the European Union
- Juan de la Cierva fellowship program - Spanish Ministry of Economy and Competitiveness [FJCI-2015-26071]
The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average - Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations,of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia - nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale. (C) 2016 Elsevier Ltd. All rights reserved.
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